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Identifying the stiffness and damping of active magnetic bearings(AMBs)is necessary since those parameters can affect the stability and performance of the high-speed rotor AMBs system.A new identification method is proposed to identify the stiffness and damping coefficients of a rotor AMB system.This method combines the global optimization capability of the genetic algorithm(GA)and the local search ability of Nelder-Mead simplex method.The supporting parameters are obtained using the hybrid GA based on the experimental unbalance response calculated through the transfer matrix method.To verify the identified results,the experimental stiffness and damping coefficients are employed to simulate the unbalance responses for the rotor AMBs system using the finite element method.The close agreement between the simulation and experimental data indicates that the proposed identified algorithm can effectively identify the AMBs supporting parameters.
Identifying the stiffness and damping of active magnetic bearings (AMBs) is necessary since those parameters can affect the stability and performance of the high-speed rotor AMBs system. A new identification method is proposed to identify the stiffness and damping coefficients of a rotor AMB system . This method combines the global optimization capability of the genetic algorithm (GA) and the local search ability of Nelder-Mead simplex method. The supporting parameters are obtained using the hybrid GA based on the experimental unbalance response calculated through the transfer matrix method. To verify the identified results, the experimental stiffness and damping coefficients are employed to simulate the unbalance responses for the rotor AMBs system using the finite element method.The close agreement between the simulation and experimental data that that the identified identified algorithm can effectively identify the AMBs supporting parameters.